SoroushVahidi/scidocs huggingface dataset:
One Model To Learn Them All
(2017)

Deep learning yields great results across many fields, from speech recognition, image classification, to translation. But for each problem, getting a deep model to work well involves research into the architecture and a long period of tuning. We present a single model that yields good results on a number of problems spanning multiple domains. In particular, this single model is trained concurrently on ImageNet, multiple translation tasks, image captioning (COCO dataset), a speech recognition corpus, and an English parsing task. Our model architecture incorporates building blocks from multiple domains. It contains convolutional layers, an attention mechanism, and sparsely-gated layers. Each of these computational blocks is crucial for a subset of the tasks we train on. Interestingly, even if a block is not crucial for a task, we observe that adding it never hurts performance and in most cases improves it on all tasks. We also show that tasks with less data benefit largely from joint training with other tasks, while performance on large tasks degrades only slightly if at all.
Incremental LSTM-based dialog state tracker
(2015)

A dialog state tracker is an important component in modern spoken dialog systems. We present an incremental dialog state tracker, based on LSTM networks. It directly uses automatic speech recognition hypotheses to track the state. We also present the key non-standard aspects of the model that bring its performance close to the state-of-the-art and experimentally analyze their contribution: including the ASR confidence scores, abstracting scarcely represented values, including transcriptions in the training data, and model averaging.
Application of neural networks to inverse lens distortion modelling
(2010)

The accurate and quick modelling of inverse lens distortion to rectify images or predict the image coordinates of real world objects has long been a challenge. This work investigates the use of artificial neural networks to perform this modelling. Several architectures are investigated and multiple methods of training the networks are used to achieve the best results. The error is expressed as a physical displacement on the imaging chip so that a fair comparison can be made between other published results which are based on cameras with different resolutions. It is shown that the novel application of locally optimised neural networks to residual lens calibration data yields an inverse distortion modelling that is highly competitive with prior published results.
Quantitative function for community detection.
(2008)

We propose a quantitative function for community partition -- i.e., modularity density or D value. We demonstrate that this quantitative function is superior to the widely used modularity Q and also prove its equivalence with the objective function of the kernel k means. Both theoretical and numerical results show that optimizing the new criterion not only can resolve detailed modules that existing approaches cannot achieve, but also can correctly identify the number of communities.
Learning Rapid-Temporal Adaptations
(2017)

A hallmark of human intelligence and cognition is its flexibility. One of the longstanding goals in AI research is to replicate this flexibility in a learning machine. In this work we describe a mechanism by which artificial neural networks can learn rapid-temporal adaptation – the ability to adapt quickly to new environments or tasks – that we call adaptive neurons. Adaptive neurons modify their activations with task-specific values retrieved from a working memory. On standard metalearning and few-shot learning benchmarks in both vision and language domains, models augmented with adaptive neurons achieve state-of-the-art results.
Exemplary Automotive Attack Scenarios : Trojan Horses for Electronic Throttle Control System ( ETC ) and Replay Attacks on the Power Window System
(2007)

The consideration of targeted security attacks is not yet common in the automotive domain where primarily safety requirements are considered. Hence this survey addresses the relatively new field of IT-security in automotive technology in order to motivate further research. With the emergence of automotive technologies like car-to-car (c2c) communication, the challenges increase. In order to show the necessity for such research we describe how in theory, a potential attack on the Electronic Throttle Control (ETC) could succeed, if no countermeasures are taken, with potentially drastic consequences on the safety of the vehicle, its occupants and its environment as a result. Also an attack on the power window system is shown using the implementation techniques already employed in the design of automotive components. Our goal is to show, that such exploited security vulnerabilities are likely to have serious consequences with respect to the vehicles safety. Therefore such attacks need to be considered and suitable countermeasures have to be identified. In order to achieve this, the connection of the two disciplines Safety and Security for automotive IT components needs to be discussed. We introduce two exemplary attacks, the Trojan Horse targeting at the Electronic Throttle Control System (ETC) and a replay attack on the electric window lift. The first attack is mainly discussed in theory, the second is elaborated in a practical simulation scenario. Finally we introduce a general investigation of potential ways of intrusion and present some first practical results from tests with current automotive hardware.
Real-time object detection, localization and verification for fast robotic depalletizing
(2015)

Depalletizing is a challenging task for manipulation robots. Key to successful application are not only robustness of the approach, but also achievable cycle times in order to keep up with the rest of the process. In this paper, we propose a system for depalletizing and a complete pipeline for detecting and localizing objects as well as verifying that the found object does not deviate from the known object model, e.g., if it is not the object to pick. In order to achieve high robustness (e.g., with respect to different lighting conditions) and generality with respect to the objects to pick, our approach is based on multi-resolution surfel models. All components (both software and hardware) allow operation at high frame rates and, thus, allow for low cycle times. In experiments, we demonstrate depalletizing of automotive and other prefabricated parts with both high reliability (w.r.t. success rates) and efficiency (w.r.t. low cycle times).
Tagging Patient Notes With ICD-9 Codes
(2017)

There is substantial growth in the amount of medical data being generated in hospitals. With over 96% adoption rate [1], Electronic Medical/Health Records are used to store most of this medical data. If harnessed correctly, this medium provides a very convenient platform for secondary data analysis of these records to improve medical and patient care. One crucial feature of the information stored in these systems are ICD9-diagnosis codes, which are used for billing purposes and integration to other databases. These codes are assigned to medical text and require expert annotators with experience and training. In this paper we formulate this problem as a multi-label classification problem and propose a deep learning framework to classify the ICD-9 codes a patient is assigned at the end of a visit. We demonstrate that a simple LSTM model with a single layer of non-linearity can learn to classify patient notes with their corresponding ICD-9 labels moderately well.
Visualization of patent analysis for emerging technology
(2008)

Many methods have been developed to recognize those progresses of technologies, and one of them is to analyze patent information. And visualization methods are considered to be proper for representing patent information and its analysis results. However, current visualization methods for patent analysis patent maps have some drawbacks. Therefore, we propose an alternative visualization method in this paper. With colleted keywords from patent documents of a target technology field, we cluster patent documents by the k-Means algorithm. With the clustering results, we form a semantic network of keywords without respect of filing dates. And then we build up a patent map by rearranging each keyword node of the semantic network according to its earliest filing date and frequency in patent documents. Our approach contributes to establishing a patent map which considers both structured and unstructured items of a patent document. Besides, differently from previous visualization methods for patent analysis, ours is based on forming a semantic network of keywords from patent documents. And thereby it visualizes a clear overview of patent information in a more comprehensible way. And as a result of those contributions, it enables us to understand advances of emerging technologies and forecast its trend in the future. 2007 Elsevier Ltd. All rights reserved.
Perceptions of Sex, Gender, and Puberty Suppression: A Qualitative Analysis of Transgender Youth
(2016)

International guidelines recommend the use of Gonadotropin-Releasing Hormone (GnRH) agonists in adolescents with gender dysphoria (GD) to suppress puberty. Little is known about the way gender dysphoric adolescents themselves think about this early medical intervention. The purpose of the present study was (1) to explicate the considerations of gender dysphoric adolescents in the Netherlands concerning the use of puberty suppression; (2) to explore whether the considerations of gender dysphoric adolescents differ from those of professionals working in treatment teams, and if so in what sense. This was a qualitative study designed to identify considerations of gender dysphoric adolescents regarding early treatment. All 13 adolescents, except for one, were treated with puberty suppression; five adolescents were trans girls and eight were trans boys. Their ages ranged between 13 and 18 years, with an average age of 16 years and 11 months, and a median age of 17 years and 4 months. Subsequently, the considerations of the adolescents were compared with views of clinicians treating youth with GD. From the interviews with the gender dysphoric adolescents, three themes emerged: (1) the difficulty of determining what is an appropriate lower age limit for starting puberty suppression. Most adolescents found it difficult to define an appropriate age limit and saw it as a dilemma; (2) the lack of data on the long-term effects of puberty suppression. Most adolescents stated that the lack of long-term data did not and would not stop them from wanting puberty suppression; (3) the role of the social context, for which there were two subthemes: (a) increased media-attention, on television, and on the Internet; (b) an imposed stereotype. Some adolescents were positive about the role of the social context, but others raised doubts about it. Compared to clinicians, adolescents were often more cautious in their treatment views. It is important to give voice to gender dysphoric adolescents when discussing the use of puberty suppression in GD. Otherwise, professionals might act based on assumptions about adolescents' opinions instead of their actual considerations. We encourage gathering more qualitative research data from gender dysphoric adolescents in other countries.
Good genes, complementary genes and human mate preferences
(2007)

The past decade has witnessed a rapidly growing interest in the biological basis of human mate choice. Here we review recent studies that demonstrate preferences for traits which might reveal genetic quality to prospective mates, with potential but still largely unknown influence on offspring fitness. These include studies assessing visual, olfactory and auditory preferences for potential good-gene indicator traits, such as dominance or bilateral symmetry. Individual differences in these robust preferences mainly arise through within and between individual variation in condition and reproductive status. Another set of studies have revealed preferences for traits indicating complementary genes, focussing on discrimination of dissimilarity at genes in the major histocompatibility complex (MHC). As in animal studies, we are only just beginning to understand how preferences for specific traits vary and inter-relate, how consideration of good and compatible genes can lead to substantial variability in individual mate choice decisions and how preferences expressed in one sensory modality may reflect those in another. Humans may be an ideal model species in which to explore these interesting complexities.
Anatomy of Forehead, Glabellar, Nasal and Orbital Muscles, and Their Correlation with Distinctive Patterns of Skin Lines on the Upper Third of the Face: Reviewing Concepts
(2016)

The purpose of this study is to establish a relationship between the skin lines on the upper third of the face in cadavers, which represent the muscle activity in life and the skin lines achieved by voluntary contraction of the forehead, glabellar, and orbital muscles in patients. Anatomical dissection of fresh cadavers was performed in 20 fresh cadavers, 11 females and 9 males, with ages ranging from 53 to 77 years. Subcutaneous dissection identified the muscle shape and the continuity of the fibers of the eyebrow elevator and depress muscles. Subgaleal dissection identified the cutaneous insertions of the muscles. They were correlated with skin lines on the upper third of the face of the cadavers that represent the muscle activity in life. Voluntary contraction was performed by 20 voluntary patients, 13 females and 7 males, with ages ranging from 35 to 62 years. Distinct patterns of skin lines on the forehead, glabellar and orbital areas, and eyebrow displacement were identified. The frontalis exhibited four anatomical shapes with four different patterns of horizontal parallel lines on the forehead skin. The corrugator supercilii showed three shapes of muscles creating six patterns of vertical glabellar lines, three symmetrical and three asymmetrical. The orbicularis oculi and procerus had single patterns. The skin lines exhibited in voluntary contraction of the upper third of the face in patients showed the same patterns of the skin lines achieved in cadavers. Skin lines in cadavers, which are the expression of the muscle activity in life, were similar to those achieved in the voluntary contraction of patients, allowing us to assert that the muscle patterns of patients were similar to those identified in cadavers. This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors http://www.springer.com/00266 .
Internet addiction: a systematic review of epidemiological research for the last decade.
(2014)

In the last decade, Internet usage has grown tremendously on a global scale. The increasing popularity and frequency of Internet use has led to an increasing number of reports highlighting the potential negative consequences of overuse. Over the last decade, research into Internet addiction has proliferated. This paper reviews the existing 68 epidemiological studies of Internet addiction that (i) contain quantitative empirical data, (ii) have been published after 2000, (iii) include an analysis relating to Internet addiction, (iv) include a minimum of 1000 participants, and (v) provide a full-text article published in English using the database Web of Science. Assessment tools and conceptualisations, prevalence, and associated factors in adolescents and adults are scrutinised. The results reveal the following. First, no gold standard of Internet addiction classification exists as 21 different assessment instruments have been identified. They adopt official criteria for substance use disorders or pathological gambling, no or few criteria relevant for an addiction diagnosis, time spent online, or resulting problems. Second, reported prevalence rates differ as a consequence of different assessment tools and cut-offs, ranging from 0.8% in Italy to 26.7% in Hong Kong. Third, Internet addiction is associated with a number of sociodemographic, Internet use, and psychosocial factors, as well as comorbid symptoms and disorder in adolescents and adults. The results indicate that a number of core symptoms (i.e., compulsive use, negative outcomes and salience) appear relevant for diagnosis, which assimilates Internet addiction and other addictive disorders and also differentiates them, implying a conceptualisation as syndrome with similar etiology and components, but different expressions of addictions. Limitations include the exclusion of studies with smaller sample sizes and studies focusing on specific online behaviours. Conclusively, there is a need for nosological precision so that ultimately those in need can be helped by translating the scientific evidence established in the context of Internet addiction into actual clinical practice.
Analysis and Characterisation of Spoof Surface Plasmon Polaritons based Wideband Bandpass Filter at Microwave Frequency
(2018)

This paper presents the wideband bandpass filter (BPF) in the microwave frequency domain. The realisation approach is based on spoof surface plasmon polaritons (SSPPs) phenomenon using plasmonic metamaterial. A novel unit cell is designed for filter design using an LC resonator concept. Then SSPPs BPF is realised using an optimised mode converter and five unit cells. This paper includes a brief design detail of the proposed novel unit cell. The passband of BPF is achieved at approximately 1.20 5.80 GHz, 3dB bandwidth is tentatively 4.60 GHz and the insertion loss is less than 2 dB approximately over the passband. The overall dimension of fabricated filter is (90 x 45) mm. A basic schematic of transmission line representation is also proposed to evaluate the BPF structure.
Hybrid Electric Vehicles: Architecture and Motor Drives
(2007)

Electric traction is one of the most promising technologies that can lead to significant improvements in vehicle performance, energy utilization efficiency, and polluting emissions. Among several technologies, hybrid electric vehicle (HEV) traction is the most promising technology that has the advantages of high performance, high fuel efficiency, low emissions, and long operating range. Moreover, the technologies of all the component hardware are technically and markedly available. At present, almost all the major automotive manufacturers are developing hybrid electric vehicles, and some of them have marketed their productions, such as Toyota and Honda. This paper reviews the present technologies of HEVs in the range of drivetrain configuration, electric motor drives, and energy storages
Learning from other subjects helps reducing Brain-Computer Interface calibration time
(2010)

A major limitation of Brain-Computer Interfaces (BCI) is their long calibration time, as much data from the user must be collected in order to tune the BCI for this target user. In this paper, we propose a new method to reduce this calibration time by using data from other subjects. More precisely, we propose an algorithm to regularize the Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) algorithms based on the data from a subset of automatically selected subjects. An evaluation of our approach showed that our method significantly outperformed the standard BCI design especially when the amount of data from the target user is small. Thus, our approach helps in reducing the amount of data needed to achieve a given performance level.
Location Segmentation, Inference and Prediction for Anticipatory Computing
(2009)

This paper presents an analysis of continuous cellular tower data representing five months of movement from 215 randomly sampled subjects in a major urban city. We demonstrate the potential of existing community detection methodologies to identify salient locations based on the network generated by tower transitions. The tower groupings from these unsupervised clustering techniques are subsequently validated using data from Bluetooth beacons placed in the homes of the subjects. We then use these inferred locations as states within several dynamic Bayesian networks to predict each subject’s subsequent movements with over 90% accuracy. We also introduce the X-Factor model, a DBN with a latent variable corresponding to abnormal behavior. We conclude with a description of extensions for this model, such as incorporating additional contextual and temporal variables already being logged by the
A Novel Self-Reference Technique for STT-RAM Read and Write Reliability Enhancement
(2014)

Spin-transfer torque random access memory (STT-RAM) has demonstrated great potential in embedded and stand-alone applications. However, process variations and thermal fluctuations greatly influence the operation reliability of STT-RAM and limit its scalability. In this paper, we propose a new field-assisted access scheme to improve the read/write reliability and performance of STT-RAM. During read operations, an external magnetic field is applied to a magnetic tunneling junction (MTJ) device, generating a resistive sense signal without referring to other devices. Such a self-reference scheme offers a very promising alternative approach to overcome the severe cell-to-cell variations at highly scaled technology node. Furthermore, the external magnetic field can be used to assist the MTJ switching during write operations without introducing extra hardware overhead.
Tendinous insertion of semimembranosus muscle into the lateral meniscus
(1997)

Forty-two cadaver knees were used for morphologic and MRI observations of the tendinous distal expansions of the semimembranosus m. and the posterior capsular structures of the knee. A tendinous branch of the semimembranosus m. inserting into the posterior horn of the lateral meniscus was found in 43.2% of the knees dissected, besides five already known insertional branches; capsular, direct, anterior and inferior, as well as the oblique popliteal ligament. The tendon had three morphologic types; thin, broad and round. All three types moved the lateral meniscus posteriorly when pulled on. Thus, the semimembranosus m. may also have a protective function for the lateral meniscus as well as the already well established function of protecting the medial meniscus in knee flexion. When a semimembranosus tendon attachment to the posterior horn of the lateral meniscus is present, its normal insertion is difficult to differentiate from a lateral meniscus tear in MRI and this may cause misdiagnosis.
Millisecond Coupling of Local Field Potentials to Synaptic Currents in the Awake Visual Cortex
(2016)

The cortical local field potential (LFP) is a common measure of population activity, but its relationship to synaptic activity in individual neurons is not fully established. This relationship has been typically studied during anesthesia and is obscured by shared slow fluctuations. Here, we used patch-clamp recordings in visual cortex of anesthetized and awake mice to measure intracellular activity; we then applied a simple method to reveal its coupling to the simultaneously recorded LFP. LFP predicted membrane potential as accurately as synaptic currents, indicating a major role for synaptic currents in the relationship between cortical LFP and intracellular activity. During anesthesia, cortical LFP predicted excitation far better than inhibition; during wakefulness, it predicted them equally well, and visual stimulation further enhanced predictions of inhibition. These findings reveal a central role for synaptic currents, and especially inhibition, in the relationship between the subthreshold activity of individual neurons and the cortical LFP during wakefulness.